2025 Pre-College Summer Course | Computer Science: Large Language Models
2025 Pre-College Summer Course｜Taught by a University of Toronto Professor｜Computer Science: Large Language Models
Are you interested in Computer Science and Large Language Models?
Gain insight into the academic and research environment of computer science undergraduates.
Build a strong foundation in computer science and artificial intelligence.
Prepare for a future major in computer science-related fields.
Explore cutting-edge AI application models.
Dive into the fields of natural language processing and artificial intelligence.
You will experience:
Personalized guidance from renowned researcher Professor Penn of a top university.
A 15-day intensive program designed to build knowledge and inspire potential.
Collaborative learning in a small class of 6–12 students.
40 hours of high-quality, intensive instruction.
Individual mentoring to develop a personalized project.
Course Content
Delve into Large Language Models (LLMs) and understand how these technologies drive modern AI, including:
The concept of language structures and their role in AI.
What are language models, and how do they work?
The mechanics behind chatbots and their distinction from Q&A systems.
The role of parsers and their applications in natural language processing (NLP).
How to represent the semantic structure of sentences.
The training process of LLMs and how they learn from data.
Ethical considerations and challenges in deploying LLMs, such as bias and misinformation.
This unique opportunity allows high school students to explore the intersection of computer science and AI in depth.
Gain knowledge of academic terminology to explore NLP tools globally.
This is an excellent starting point for high school students planning to study computer science and AI in higher education.
Many major companies, such as Baidu and Microsoft, have NLP research divisions that develop products like search systems, translation software, and chatbots.
What You Will Gain
❝ Gain an in-depth understanding of Large Language Models (LLMs). ❞
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❝ Learn AI applications such as chatbots, Q&A systems, and privacy protection. ❞
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❝ Build foundational knowledge of key NLP concepts like language structures, models, and parsers. ❞
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❝ Develop practical skills and foundational knowledge applicable to various fields. ❞
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❝ Prepare for advanced studies in computer science and AI. ❞
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❝ Receive a certificate of completion upon successfully finishing the course. ❞
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❝ Create a personalized project tailored to your interests. ❞
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❝ Opportunity to receive a personalized recommendation letter from the professor. ❞
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— About Professor Gerald Penn —
Prof. Gerald Penn
- University of Toronto -
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Professor Gerald Penn is a renowned computer scientist and AI expert. He has made significant contributions to the fields of Natural Language Processing (NLP) and Machine Learning, as well as to the development of computational linguistics tools and applications.
| Academic Achievements |
Professor Gerald Penn holds a Ph.D. in Computer Science from Carnegie Mellon University and currently serves as a professor in the Department of Computer Science at the University of Toronto. He has also been a visiting scholar at esteemed institutions such as the University of Edinburgh, Université de Paris, and the University of Maryland.
| Research Contributions |
Professor Penn has conducted extensive research in Natural Language Processing (NLP) and Machine Learning, with a particular focus on parsing, semantics, and syntax. His groundbreaking work, published in leading conferences and journals, has significantly advanced the field of NLP.
| Innovative Achievements |
He has developed critical computational linguistics tools, including the OpenCCG parser, widely used in NLP research and applications. He also contributed to the development of the Link Grammar Parser and OpenNLP toolkit, both highly regarded in academia and industry.
| Industry Contributions |
Professor Penn has served as a consultant and advisory board member for major companies such as Google and IBM, providing expertise in NLP and Machine Learning. Additionally, he co-founded startups like Talk Science, which focuses on developing tools for scientific communication.
—— Course Schedule ——
July 2 – July 18, 2025
July 2 – 5 (Wed – Sat) 9 AM – 12 PM
July 7 – 12 (Mon – Sat) 9 AM – 12 PM
July 14 – 18 (Mon – Fri) 9 AM – 11 AM
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